Tableau Reviews

My main use cases with Tableau include working on the migration of dashboards from legacy systems to cloud-based solutions, such as migrating from Looker to Tableau Dashboard or Power BI to Tableau Dashboard. Recently, since the acquisition by Salesforce, the demand in the Tableau space has decreased compared to Power BI, as many clients are moving towards Power BI due to its more affordable licensing and the appealing use cases that it offers.

Tableau has recently announced the Tableau Next platform, which is exciting, but there are still uncertainties about how it will pan out compared to other BI tools in the market. Currently, our focus is primarily on dashboard development and migration between platforms, setting up business architectures, recommending tools based on user requirements, and conducting benchmarking to understand market offerings. Tableau is my main tool, and I continuously explore what's happening in the space to determine if Tableau is performing better or what innovations are on the horizon.

When comparing Tableau to other BI tools, they are generally on par, but the drag-and-drop feature in Tableau is user-friendly, and it handles large data sets more effectively than Power BI. Tableau's ability to manage extensive data, even in an extract format, is noteworthy, as Power BI struggles with that.

Creating dashboards and performing calculations in Tableau is quite straightforward and provides a better user experience compared to Power BI, which can feel more outdated. Although Tableau comes at a premium, the performance on the cloud platform and the overall ease of use for developers are significant advantages, even though it isn't designed for tabular reporting some users, particularly in finance, desire.

I am supporting a product company for market analysis and market research where we are using Tableau.

For retargeting and validating purchasing decisions, we use AI to create visualizations for predicting spending habits for the next six months for different categories.

The most effective features of Tableau for data analysis are visualization, comparison, and data analysis, which is much practical in this kind of project.

We are using AI features in Tableau.

For retargeting and validating purchasing decisions, we use AI to create visualizations for predicting spending habits for the next six months for different categories.

We are majorly using it for our procurement business area. We have created almost 50 to 60 dashboards for the entire procurement cycle. We have used it for procure-to-pay, and we have also used it for our retail business. We have a lot of petrol bunks, and we get a lot of data from them regarding sales and other things. So, procure-to-pay and retail business are the main use cases.

We've been using its latest update. It is on-premise. All our solutions are on-prem because we are in the Oil and Gas sector, and data is very critical for us. We have not yet migrated anything to the cloud.

It is very easy to build charts and drag and drop the fields that are there. It automatically identifies the dimensions and measures and makes our life easier when we need to build any dashboard. It is pretty user-friendly.

Its visualizations are good, and its features make the development process a little less time-consuming. It has an in-memory extract feature that allows us to extract data and keep it on the server, and then our users can use it quickly.

I've used Tableau primarily to visualize data on asset performance. These visualizations pertain to reliability engineering and I've created charts and dashboards showing key performance indicators such as mean time between failure for different asset components, trend/pattern of asset behavior (different types of events occurred in the asset) prior to failure and asset grouping based on their health and performance.

Tableau is so flexible as to enable the user to show events that have occurred over the entire lifespan of each asset. Normally, this requires a bit of data-wrangling but in my experience this graphic has found a lot of favor with multiple clients.

All of these visualizations were created in a local environment (at the desktop-level) using data from .xlsx and .csv files.

In our line of work, we primarily use Tableau Desktop/Server to visualize data based on our clients’ requirements. Once, while dealing with a procurement scenario, we found that the client could save $2.00 on each unit of Part A if they ordered it from Supplier X rather than from Supplier Y.

In another case, we designed dashboards that showed data from different sensors located throughout a building. This kind of an application could enable stakeholders to monitor building climatic conditions in real time and adjust thermostats according to CO2 levels and occupancy, thereby driving efficient consumption of power.

In this way, Tableau visualizations can be used to take more intelligent decisions as they bring out patterns buried under a mountain of data.

I use it for sales tracking, incident management, and monitoring proactive tickets from different tools and systems. These are my three major use cases.

The biggest benefit is significant time savings in creating reports and dashboards. We now spend less time creating dashboards and reports. It's also very user-friendly and easy to work with.